UnsDrive：面向非结构化场景的鲁棒端到端自动驾驶
UnsDrive: Towards Robust End-to-End Autonomous Driving in Unstructured Scenes
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中文总结 AI 辅助
针对非结构化采矿环境自动驾驶泛化差的问题，提出UnsDrive规划器，结合未知感知占用表示与流匹配规划器，引入专用损失和评分器，辅以MineLoop模拟器验证，性能优于基线。
中文摘要 AI 辅助
端到端规划在自动驾驶领域展现出强大潜力，但现有多数方法针对结构化城市道路设计，在非结构化采矿环境中泛化能力较差。此类场景中，道路结构薄弱、地形导致的遮挡、能见度下降及大量未观测区域，使安全规划极具挑战性。为应对这些挑战，我们提出UnsDrive，一种专为非结构化采矿场景设计的端到端规划器。UnsDrive构建了一种感知未知的占用表示，利用多帧可见性线索显式建模已占用、空闲及未知空间，并基于该表示条件化流匹配规划器，以生成多模态未来轨迹。为提升部分可观测性下的安全性，我们进一步引入占用轨迹一致性损失和感知不确定性的轨迹评分器，对进入不可通行或未观测区域的轨迹进行惩罚。我们还推出MineLoop，一种面向采矿的闭环模拟器，用于在不规则道路几何、能见度下降、重型车辆交互及采矿特定操作约束下评估自动驾驶性能。开环和闭环场景下的实验表明，UnsDrive在轨迹精度、避撞及长时距驾驶鲁棒性上均显著优于强基线方法。这些结果证明了显式未知空间推理对非结构化采矿环境自动驾驶的价值。
英文摘要
End-to-end planning has shown strong promise for autonomous driving, but most existing methods are designed for structured urban roads and generalize poorly to unstructured mining environments. In such settings, weak road structure, terrain-induced occlusions, degraded visibility, and large unobserved regions make safe planning particularly challenging. To address these challenges, we propose UnsDrive, an end-to-end planner designed for unstructured mining scenes. UnsDrive builds an unknown-aware occupancy representation that explicitly models occupied, free, and unknown space using multi-frame visibility cues, and conditions a flow-matching planner on this representation to generate multimodal future trajectories. To improve safety under partial observability, we further introduce an occupancy trajectory consistency loss and an uncertainty-aware trajectory scorer that penalize trajectories entering non-traversable or unobserved regions. We also present MineLoop, a mining-oriented closed-loop simulator for evaluating autonomous driving under irregular road geometry, degraded visibility, heavy-vehicle interactions, and mining-specific operational constraints. Experiments in both open-loop and closed-loop settings show that UnsDrive consistently outperforms strong baselines in trajectory accuracy, collision avoidance, and long-horizon driving robustness. These results demonstrate the value of explicit unknown-space reasoning for autonomous driving in unstructured mining environments.
发表机构
- University of Chinese Academy of Sciences（中国科学院大学）
- Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
- Waytous Inc.（文远知行公司）
机构由 AI 辅助整理，请以论文原文为准。
